{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Using Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often text is the best way to communicate information, but sometimes there is a\n",
    "lot of truth to the Chinese proverb,\n",
    "\n",
    "**图片的意义可以表达近万字** \n",
    "\n",
    ">A picture's meaning can express ten thousand words\n",
    "\n",
    "**Matplotlib**\n",
    "\n",
    "http://matplotlib.org/\n",
    "\n",
    "Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.\n",
    "\n",
    "Matplotlib Developers on Github: https://github.com/matplotlib\n",
    "\n",
    "User's Guide: http://matplotlib.org/users/index.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Matplotlib.pyplot\n",
    "\n",
    "https://matplotlib.org/2.0.2/api/pyplot_api.html\n",
    "\n",
    "**Matplotlib.pyplot** provides a `MATLAB`-like plotting framework.\n",
    "\n",
    "### 1.1 The Simple Example\n",
    "Let’s start with a simple example that uses `pyplot.plot` to produce the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ./code/python/plt111.py\n"
     ]
    }
   ],
   "source": [
    "%%file ./code/python/plt111.py\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure() #create figure \n",
    "plt.plot([1,2,3,4], [1,7,3,5]) #draw on figure 1 <x,y> list/array\n",
    "plt.show() #show figure on screen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    ">python plt111.py\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![figure1](./img/c11-figure1.jpg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure() #create figure 1\n",
    "x=[1,2,3,4]\n",
    "y=[1,7,3,5]\n",
    "plt.plot(x,y) # plot x and y using default line style and color\n",
    "plt.show() #show figure on screen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 The Basic Method of PyPlot\n",
    "\n",
    "* pyplot.figure()\n",
    "\n",
    "* pyplot.plot(x,y)\n",
    "\n",
    "* pyplot.show()\n",
    "\n",
    "#### 1.2.1 [pyplot.figure ](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure) \n",
    "\n",
    "Create a new figure.\n",
    "\n",
    "```python\n",
    "matplotlib.pyplot.figure(num=None)\n",
    "``` \n",
    "\n",
    "**num** : integer or string, optional, default: ```None```\n",
    "\n",
    "The example,the num is not provided, a new figure will be created,\n",
    "\n",
    "#### 1.2.2 [ pyplot.plot]\n",
    "(https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot)\n",
    "\n",
    "Plot <x,y>(y versus x) as lines and/or markers\n",
    "```python\n",
    " matplotlib.pyplot.plot(x, y)\n",
    "``` \n",
    "![plotxy](./img/plotxy.jpg)\n",
    "  \n",
    "  \n",
    "#### 1.2.3 [pyplot.show](https://matplotlib.org/devdocs/api/_as_gen/matplotlib.pyplot.show.html)\n",
    "\n",
    "**Display a figure**. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) # create figure  with number 1\n",
    "x=[1,2,3,4]\n",
    "y=[1,7,3,5]\n",
    "plt.plot(x,y) # plot x and y using blue circle markers\n",
    "plt.show() # show figure on screen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Multiple figures & write them to files\n",
    "\n",
    "#### 1.3.1 Multiple figures\n",
    "\n",
    "Create a new figure.\n",
    "\n",
    "```python\n",
    "matplotlib.pyplot.figure(num=None)\n",
    "``` \n",
    "\n",
    "**num** : integer or string, optional, default: ```None```\n",
    "\n",
    "* If not provided, a new figure will be created, and the figure number will be incremented. The figure objects holds this number in a number attribute.\n",
    "\n",
    "* If num is provided, \n",
    "  * If this figure does not exists, create it and returns it. \n",
    "  * If a figure with this id already exists, make it active, and returns a reference to it.\n",
    "  * If num is a string, the window title will be set to this figure's num\n",
    "\n",
    "It is possible to produce **multiple figures** \n",
    "\n",
    "Tne next example produces tow figures:**1,2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# create figure 1\n",
    "plt.figure(1)\n",
    "plt.plot([1,2,3,4], [1,2,3,4]) # plot on figure 1\n",
    "\n",
    "# create figure 2\n",
    "plt.figure(2) \n",
    "plt.plot([1,4,2,3], [5,6,7,8]) # plot on figure 2\n",
    "\n",
    "# figure 1 id already exists, make figure 1 active\n",
    "# and returns a reference to it\n",
    "#   Go back to figure 1 and plotting again \n",
    "plt.figure(1)\n",
    "# Plot again on figure 1\n",
    "plt.plot([5,6,10,3]) #  plot(y) on  figure 1\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. create figure 1: ```plt.figure(1)```\n",
    "\n",
    "2. create figure 2: ```plt.figure(2)```\n",
    "\n",
    "3. Go back and plotting on figure 1 ```plt.figure(1)```\n",
    "\n",
    "```python\n",
    " plot(y)\n",
    "```\n",
    "##### pyplot.plot(y)\n",
    "\n",
    "plot $y$ using $x$ as index array $0..N-1$,using default line style and color\n",
    "\n",
    "* `pyplot.plot([5,6,10,3]) # plot again on figure 1`\n",
    "\n",
    "   The corresponding $x$ values default to `range(len([5, 6, 10, 3]))`( 0 to 3 in this case  plot $y$ using $x$ as index array$ 0..N-1$ \n",
    "\n",
    "**Figure 1**\n",
    "\n",
    "Two lines: \n",
    "```python\n",
    "plt.plot([1,2,3,4], [1,2,3,4]) \n",
    "\n",
    "# Go back and plotting on figure 1\n",
    "plt.plot([5,6,10,3])\n",
    "```\n",
    "![figure1](./img/Figure1.svg)\n",
    "\n",
    "**Figure 2**\n",
    "\n",
    "One line: \n",
    "\n",
    "```python\n",
    "plt.plot([1,4,2,3], [5,6,7,8])\n",
    "```\n",
    "\n",
    "![figure2](./img/Figure2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3.2 Write figure to files\n",
    "\n",
    "```python\n",
    "plt.savefig(figurefilename)\n",
    "```\n",
    "\n",
    "These files can have any name you like. \n",
    "\n",
    "They will all have the file extension` .png` in the default.\n",
    "\n",
    "* `.png` indicates that the file is in the `Portable Networks Graphics` format. This is a public domain standard for representing images\n",
    "\n",
    "You can set the figure file format,for example,\n",
    "\n",
    "**To save the plot as an SVG**\n",
    "\n",
    "[Scalable Vector Graphics (SVG)](https://en.wikipedia.org/wiki/Scalable_Vector_Graphics) is an XML-based vector image format for two-dimensional graphics with support for interactivity and animation. The SVG specification is an open standard developed by the World Wide Web Consortium (W3C) since 1999. \n",
    "\n",
    "All major modern web browsers—including Mozilla Firefox, Internet Explorer, Google Chrome, Opera, Safari, and Microsoft Edge—have SVG rendering support. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(1) #create figure 1\n",
    "plt.plot([1,2,3,4], [1,2,3,4]) # plot on figure 1\n",
    "\n",
    "plt.figure(2) #create figure 2\n",
    "plt.plot([1,4,2,3], [5,6,7,8]) # plot on figure 2\n",
    "#save figure 2 without extension,to the default .png\n",
    "plt.savefig('./img/Figure2') \n",
    "\n",
    "#go back to plot working on figure 1\n",
    "plt.figure(1)\n",
    "# plot again on figure 1\n",
    "plt.plot([5,6,10,3]) #  # plot y using x as index array 0..N-1,using default line style and color\n",
    "\n",
    "# save figure 1  as an SVG\n",
    "plt.savefig('./img/Figure11.svg') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive F is cmh\n",
      " Volume Serial Number is 9C25-3306\n",
      "\n",
      " Directory of F:\\SEU\\SEE\\PySEE\\home\\notebook\\img\n",
      "\n",
      "2019/11/21  12:24            11,026 Figure1.png\n",
      "2020/04/13  16:53            15,622 Figure1.svg\n",
      "2020/04/13  16:54            15,622 Figure11.svg\n",
      "2020/04/13  16:54            12,117 Figure2.png\n",
      "2019/11/21  12:24            11,714 Figure2.svg\n",
      "2019/11/21  12:24           146,585 figure412.jpg\n",
      "               6 File(s)        212,686 bytes\n",
      "               0 Dir(s)  92,567,728,128 bytes free\n"
     ]
    }
   ],
   "source": [
    "!dir .\\img\\Figure*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Title,xlabel,ylabel \n",
    " \n",
    "Let’s look at another example of `the growth of an initial investment of $10,000 at an annually 5%`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "principal = 10000 #initial investment\n",
    "interestRate = 0.05\n",
    "years = 20\n",
    "values = []\n",
    "\n",
    "for i in range(years + 1):\n",
    "    values.append(principal)\n",
    "    principal += principal*interestRate\n",
    "\n",
    "plt.plot(values) # plot y using x as index array 0..N-1,using default line style and color\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we **look at the code**,\n",
    "\n",
    "* this cannot be easily inferred by looking **only at the plot `itself`**. That’s a bad thing.\n",
    "\n",
    "**All plots should have**\n",
    "\n",
    "* `informative` **titles** \n",
    "\n",
    "* all **axes** should be `labeled`.\n",
    "\n",
    "If we add to the end of our the code the lines\n",
    "```\n",
    "plt.title('5% Growth, Compounded Annually')\n",
    "plt.xlabel('Years of Compounding')\n",
    "plt.ylabel('Value of Principal ($)')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "principal = 10000 #initial investment\n",
    "interestRate = 0.05\n",
    "years = 20\n",
    "values = []\n",
    "\n",
    "for i in range(years + 1):\n",
    "    values.append(principal)\n",
    "    principal += principal*interestRate\n",
    "\n",
    "plt.plot(values) # plot y using x as index array 0..N-1,using default line style and color\n",
    "\n",
    "# add tile,xlabel,ylable\n",
    "plt.title('5% Growth, Compounded Annually')\n",
    "plt.xlabel('Years of Compounding')\n",
    "plt.ylabel('Value of Principal ($)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Formating plotted curve\n",
    "\n",
    "#### 1.5.1 Line and marker\n",
    "\n",
    "##### 1.5.1.1 The color, type, marker symbols\n",
    "For every plotted curve, there is an optional argument that is **a format string** indicating\n",
    "\n",
    "**the `color`，line `type` and marker `symbols` of the plot**\n",
    "\n",
    "1. The first character is <b style=\"color:red\">color</b>: <b style=\"color:blue\"> b </b> blue \n",
    "\n",
    "2. The following characters are <b style=\"color:red\">line type</b>:  <b style=\"color:blue\"> - </b> solid line\n",
    "\n",
    "   * possible linestype: ‘-‘, ‘--’, ‘-.’, ‘:’, \n",
    "\n",
    "\n",
    "3. The following characters are  <b style=\"color:red\">marker symbols</b>:  <b style=\"color:blue\"> + </b> symbol\n",
    "  \n",
    "   * possible marker symbols: '+', 'o', '*', 's', ',', '.', '1', '2', '3', '4', ...\n",
    "\n",
    "The **default format** string is  <b style=\"color:blue\">'b-'</b>, which produces a <b style=\"color:blue\">solid blue line</b>.\n",
    "\n",
    "If you want to plot the above with <b style=\"color:green\">green</b>, `dashed` line with `circle` marker symbol.  one would replace the call \n",
    "\n",
    "```python\n",
    "pyplot.plot(values)\n",
    "```\n",
    "by\n",
    "\n",
    "```python\n",
    "pyplot.plot(values, 'g--o')\n",
    "```\n",
    "##### 1.5.1.2 line: width\n",
    "\n",
    "To change the line width, we can use the `linewidth` or `lw` keyword argument.\n",
    "\n",
    "```python\n",
    "plt.plot(values, linewidth =3)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "principal = 10000 #initial investment\n",
    "interestRate = 0.05\n",
    "years = 20\n",
    "values = []\n",
    "for i in range(years + 1):\n",
    "    values.append(principal)\n",
    "    principal += principal*interestRate\n",
    "\n",
    "# green circle ,dashed, width = 3\n",
    "plt.plot(values,'g--o',linewidth = 1)\n",
    "\n",
    "#If we add to the end of our the code the lines\n",
    "plt.title('5% Growth, Compounded Annually')\n",
    "plt.xlabel('Years of Compounding')\n",
    "plt.ylabel('Value of Principal ($)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.5.2  type size \n",
    "\n",
    "It’s also possible to change the type `size` used in plots.\n",
    "\n",
    "For example,set `fontsize`\n",
    "```python\n",
    "plt.xlabel('Years of Compounding', fontsize = 'x-small')``\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "principal = 10000 #initial investment\n",
    "interestRate = 0.05\n",
    "years = 20\n",
    "values = []\n",
    "for i in range(years + 1):\n",
    "    values.append(principal)\n",
    "    principal += principal*interestRate\n",
    "\n",
    "#line width     \n",
    "plt.plot(values,'b--', linewidth = 2)\n",
    "\n",
    "# fontsize \n",
    "plt.title('5% Growth, Compounded Annually', fontsize = 'xx-large')\n",
    "# fontsize \n",
    "plt.xlabel('Years of Compounding', fontsize = 'x-small')\n",
    "plt.ylabel('Value of Principal ($)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fuether Reading\n",
    "\n",
    "**Matplotlib Tutorials:** https://matplotlib.org/tutorials/index.html\n",
    "\n",
    "**Pyplot tutorial** https://matplotlib.org/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python387jvsc74a57bd02db524e06e9f5f4ffedc911c917cb75e12dbc923643829bf417064a77eb14d37",
   "display_name": "Python 3.8.7 64-bit"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
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   "toc_window_display": true
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 },
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}